Several control systems employed by vehicles, either autonomous vehicles or semi-autonomous vehicles executing in autonomous-driving mode, predict future, safe motions, or paths, of the vehicle, both in order to avoid obstacles, such as other vehicles or pedestrians, and to optimize some criteria associated to the operation of the vehicle. The target state can either be a fixed location, a moving location, a velocity vector, a region, or a combination thereof. One of the tasks for controlling the autonomous or semi-autonomous vehicles is to automatically park a vehicle into a parked position and orientation referred herein as a target state.
Most existing path planning solutions only cope with specific parking scenarios. For instance, a method described in U.S. Pat. No. 7,737,866 calculates paths for parallel parking and back-in parking. The method described in U.S. Pat. No. 8,497,782 also assumes a special structure of the parking path. Also, the method described in U.S. Pat. No. 8,862,321 addresses parallel parking and requires the initial state of the vehicle to be within a so-called feasible starting region from which pre-coded parallel parking maneuvers are initiated. Although achieving real-time path generation, those methods rely on a specific structure of the parking path.
To accommodate general parking path, one method described in U.S. Pat. No. 9,140,553 uses a two-staged automatic parking system that calculates and remembers the parking path during a learning mode when a vehicle is manually parked by a driver, and later assists parking the vehicle along the leaned parking path during an auto-parking mode. This method can assist parking in specified spaces, such as residential garages, along the arbitrarily but previously learned parking path. That method considers the deviation from that parking path as an erratic course that needs to be prevented. Such a rationale is not always desirable.